Persistent homology-based gait recognition robust to upper body variations

Gait recognition is nowadays an important biometric technique for video surveillance tasks, due to the advantage of using it at distance. However, when the upper body movements are unrelated to the natural dynamic of the gait, caused for example by carrying a bag or wearing a coat, the reported results show low accuracy. With the goal of solving this problem, we apply persistent homology to extract topological features from the lowest fourth part of the body silhouettes. To obtain the features, we modify our previous algorithm for gait recognition, to improve its efficacy and robustness to variations in the amount of simplices of the gait complex. We evaluate our approach using the CASIA-B dataset, obtaining a considerable accuracy improvement of 93:8%, achieving at the same time invariance to upper body movements unrelated with the dynamic of the gait.Ministerio de Economía y Competitividad MTM2015-67072-

Topological signature for periodic motion recognition

In this paper, we present an algorithm that computes the topological signature for a given periodic motion sequence. Such signature consists of a vector obtained by persistent homology which captures the topological and geometric changes of the object that models the motion. Two topological signatures are compared simply by the angle between the corresponding vectors. With respect to gait recognition, we have tested our method using only the lowest fourth part of the body's silhouette. In this way, the impact of variations in the upper part of the body, which are very frequent in real scenarios, decreases considerably. We have also tested our method using other periodic motions such as running or jumping. Finally, we formally prove that our method is robust to small perturbations in the input data and does not depend on the number of periods contained in the periodic motion sequence.Comment: arXiv admin note: substantial text overlap with arXiv:1707.0698

Real-time tracking of multiple objects with locally adaptive correlation filters

A tracking algorithm using locally adaptive correlation filtering is proposed. The algorithm is designed to track multiple objects withinvariancetopose,occlusion,clutter,andilluminationvariations. Thealgorithmemploysapredictionschemeandcomposite correlationfilters. Thefiltersaresynthesizedwiththehelpofaniterativealgorithm,whichoptimizesdiscriminationcapabilityfor each target. The filters are adapted online to targets changes using information of current and past scene frames. Results obtained with the proposed algorithm using real-life scenes, are presented and compared with those obtained with state-of-the-art tracking methods in terms of detection efficiency, tracking accuracy, and speed of processing.This work was supported by the Russian Science Foundation, grant no. 15-19-10010

Persistent-homology-based gait recognition

Gait recognition is an important biometric technique for video surveillance tasks, due to the advantage of using it at distance. In this paper, we present a persistent homology-based method to extract topological features (the so-called topological gait signature) from the the body silhouettes of a gait sequence. It has been used before in sev- eral conference papers of the same authors for human identi cation, gender classi cation, carried object detection and monitoring human activities at distance. The novelty of this paper is the study of the sta- bility of the topological gait signature under small perturbations and the number of gait cycles contained in a gait sequence. In other words, we show that the topological gait signature is robust to the presence of noise in the body silhouettes and to the number of gait cycles con- tained in a given gait sequence. We also show that computing our topological gait signature of only the lowest fourth part of the body silhouette, we avoid the upper body movements that are unrelated to the natural dynamic of the gait, caused for example by carrying a bag or wearing a coat.Ministerio de Economía y Competitividad MTM2015-67072-

Speeding up the combination of multiple descriptors for different boundary conditions

Content-based complex data retrieval is becoming increasingly common in many types of applications. The content of these data is represented by intrinsic characteristics, extracted from them which together with a distance function allows similarity queries. Aimed at reducing the “semantic gap”, characterized by the disagreement between the computational representation of the extracted low-level features and how these data are interpreted by the human perception, the use of multiple descriptors has been the subject of several studies. This paper proposes a new method to carry out the combination of multiple descriptors for different boundary conditions in which the balancing is carried out in pairs, starting by the best candidate descriptor. In the experiments, the proposed method achieved computational cost up to 3650 times smaller than the exhaustive search for the best linear combination of descriptors, keeping almost the same average precision, with variations lower than 0.9%.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Table recognition in mathematical documents

While a number of techniques have been developed for table recognition in ordinary text documents, when dealing with tables in mathematical documents these techniques are often ineffective as tables containing mathematical structures can differ quite significantly from ordinary text tables. In fact, it is even difficult to clearly distinguish table recognition in mathematics from layout analysis of mathematical formulas. Again, it is not straight forward to adapt general layout analysis techniques for mathematical formulas. However, a reliable understanding of formula layout is often a necessary prerequisite to further semantic interpretation of the represented formulae. In this thesis, we present the necessary preprocessing steps towards a table recognition technique that specialises on tables in mathematical documents. It is based on our novel robust line recognition technique for mathematical expressions, which is fully independent of understanding the content or specialist fonts of expressions. We also present a graph representation for complex mathematical table structures. A set of rewriting rules applied to the graph allows for reliable re-composition of cells in order to identify several valid table interpretations. We demonstrate the effectiveness of our technique by applying them to a set of mathematical tables from standard text book that has been manually ground-truthed